Google Cloud PDE Sample Questions with Explanations

These original sample questions are designed to help you check how the exam topics appear in decision-style prompts. They are not taken from the live exam.

Use these sample questions as a guided self-assessment for Google Cloud Professional Data Engineer (PDE) topics such as ingestion, storage selection, pipeline design, BigQuery optimization, governance, quality, streaming, orchestration, and operational reliability.

Where these questions fit in the PDE guide

The sample set below is part of the Google Cloud PDE guide path:

• Google Cloud certification hub
• PDE exam guide
• PDE cheat sheet
• PDE study plan
• PDE FAQ
• PDE resources
• PDE glossary

PDE data-engineering sample questions

Work through each prompt before opening the explanation. Data-engineering questions usually follow the path: ingest, store, transform, govern, serve, monitor, and optimize.

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Question 1

Topic: Choosing streaming ingestion

An analytics platform receives purchase events from thousands of mobile clients. Events must be accepted durably, buffered during traffic spikes, and processed in near real time by a streaming pipeline. Which ingestion pattern is strongest?

• A. Write events to a Compute Engine VM disk and run a cron job every night.
• B. Use Cloud Storage batch uploads only, because all analytics should be file-based.
• C. Send events to Pub/Sub and process them with a streaming Dataflow pipeline.
• D. Insert every event directly into a manually managed database from the mobile app.

Best answer: C

Explanation: Pub/Sub provides durable event ingestion and buffering, while Dataflow handles streaming processing. The pair matches near-real-time processing and traffic-spike requirements.

Why the other choices are weaker:

• A is batch-oriented and creates a VM bottleneck.
• B can fit batch analytics but does not satisfy near-real-time processing.
• D tightly couples clients to the database and handles spikes poorly.

What this tests: Streaming ingestion, buffering, event processing, and managed pipeline fit.

Related topics: Pub/Sub; Dataflow; Streaming; Ingestion

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Question 2

Topic: Optimizing BigQuery cost and performance

A BigQuery table stores five years of clickstream data. Most queries filter by event date and customer region. Costs are high because analysts often scan the full table. Which table design change should be considered first?

• A. Partition by event date and cluster by region or another common filter column.
• B. Export the table to CSV before every query.
• C. Disable query caching so every query is recalculated.
• D. Store all rows in one unpartitioned table and increase user quotas.

Best answer: A

Explanation: Partitioning by the dominant date filter reduces scanned data, and clustering can improve pruning for frequent secondary filters such as region. This directly targets scan cost and query performance.

Why the other choices are weaker:

• B adds operational overhead and loses BigQuery execution benefits.
• C can increase repeated-query cost.
• D keeps the root cause and only raises limits.

What this tests: BigQuery partitioning, clustering, scan reduction, and cost-aware design.

Related topics: BigQuery; Partitioning; Clustering; Cost optimization

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Question 3

Topic: Protecting sensitive analytics data

A data warehouse contains customer identifiers and transaction history. Analysts need aggregate trends, but only a small compliance group should see direct identifiers. Which design best supports privacy and least privilege?

• A. Grant all analysts project Owner so query access is never blocked.
• B. Create governed views or authorized datasets that expose only needed fields and aggregates, and restrict direct table access to approved roles.
• C. Copy the raw table to every analyst project so teams can self-serve.
• D. Remove all audit logs so sensitive query activity is not recorded.

Best answer: B

Explanation: The design separates raw sensitive data from analyst-facing access paths. Views, authorized datasets, and role scoping preserve analytical value while limiting exposure of identifiers.

Why the other choices are weaker:

• A massively overgrants permissions.
• C spreads sensitive data and weakens governance.
• D removes accountability and investigation evidence.

What this tests: Data governance, least privilege, sensitive-field exposure, and auditability.

Related topics: Governance; BigQuery access; Privacy; Authorized views

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Question 4

Topic: Handling late-arriving data

A streaming pipeline calculates hourly metrics from event timestamps. Some mobile devices send events several minutes late. The business wants metrics to include late events when they arrive within a defined tolerance. What should the pipeline design include?

• A. Use event-time windows with an allowed lateness policy appropriate to the business tolerance.
• B. Use only processing time and ignore timestamps from the event payload.
• C. Drop every event that arrives after the first record in each hour.
• D. Run the pipeline once per month so late events no longer matter.

Best answer: A

Explanation: Event-time processing lets the pipeline group data by when the event happened, and allowed lateness defines how late data is incorporated. That matches the stated tolerance requirement.

Why the other choices are weaker:

• B measures ingestion time rather than business event time.
• C discards valid late data.
• D avoids the streaming requirement and destroys timeliness.

What this tests: Streaming windows, event time, late data, and metric correctness.

Related topics: Event time; Windows; Late data; Dataflow

Independent study note

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